{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from pyquante2 import *\n",
    "from pyquante2.scf.iterators import AveragingIterator\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def n2_mol(R): return molecule([(7,0,0,-R/2),(7,0,0,R/2)],units='Angstrom')\n",
    "def get_n2_energy(R,**kwargs): return get_energy(n2_mol(R),**kwargs)\n",
    "\n",
    "def get_energy(geo,basis='sto3g',returnall=False):\n",
    "    bfs = basisset(geo,basis)\n",
    "    solver = rhf(geo,bfs)\n",
    "    ens = solver.converge()\n",
    "    if returnall: return ens\n",
    "    return ens[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.900 -107.187\n",
      "0.950 -107.329\n",
      "0.975 -107.380\n",
      "1.000 -107.420\n",
      "1.025 -107.450\n",
      "1.050 -106.680\n",
      "1.100 -106.770\n"
     ]
    }
   ],
   "source": [
    "Rs = [0.9,0.95,0.975,1.0,1.025,1.05,1.1]\n",
    "Es = []\n",
    "for R in Rs:\n",
    "    n2 = molecule([(7,0,0,-R/2),(7,0,0,R/2)],units='Angstrom')\n",
    "    bfs = basisset(n2,'sto3g')\n",
    "    solver = rhf(n2,bfs)\n",
    "    #ens = solver.converge(AveragingIterator,maxiters=100)\n",
    "    ens = solver.converge()\n",
    "    Es.append(solver.energy)\n",
    "    print \"%.3f %.3f\" % (R,solver.energy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x736a630>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7gXt72H8IWNzD/nX4ayY45w4Cs0KJTUSGD4/Xx+yJKcTF6l3hkUx/eiISVs45PN4WStTh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\n/j8LixqpHl4y6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7540fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Rs,Es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-103.69380947097166,\n",
       " -107.4205451863952,\n",
       " -107.44870042724699,\n",
       " -107.44991410543547,\n",
       " -107.44996870578061]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_n2_energy(1.025,returnall=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-103.84150710154371,\n",
       " -106.6555720985487,\n",
       " -106.67890976939941,\n",
       " -106.67968004223863,\n",
       " -106.67971298551639]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_n2_energy(1.05,returnall=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The convergence of the SCF steps, as shown in the above two examples, is monotonic and well-behaved, and is thus *not* the problem we're seeing here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Enukes = [n2_mol(R).nuclear_repulsion() for R in Rs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7d06da0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pda3P5wPaULRABA9+uJjBE5dz9JSu1kWyQ4UunmtatRTT4tvx2LW1mLxsFze8Ppvv1+h1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\nkRDx/+gs0wxaOdnfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7b56080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Rs,Enukes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named mcscf",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mImportError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-8f25827cd4d8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpyquante2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmcscf\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mgvb\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m: No module named mcscf"
     ]
    }
   ],
   "source": [
    "from pyquante2.scf.mcscf import gvb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----GVB calculation on N2 at R=0.900\n",
      "**** PyQuante GVB ****\n",
      "Stoichiometry = N2, Charge = 0, Multiplicity = 1\n",
      "7 N     0.000000     0.000000    -0.850377\n",
      "7 N     0.000000     0.000000     0.850377\n",
      "Nuclear repulsion energy: 28.811\n",
      "Basis set: sto3g\n",
      "  ncore/open/pair: 7,0,0\n",
      "  occ/bf/orb: 7,10,10\n",
      "Shell 0\n",
      "  occupation = 1.00\n",
      "  orbitals in shell [0, 1, 2, 3, 4, 5, 6]\n",
      "  couplings to other shells [(2.0, -1.0)]\n",
      "Starting guess at orbitals:\n",
      "[[ -7.0780e-01  -7.0841e-01   1.5671e-01   9.2773e-16   3.4306e-16\n",
      "    1.5457e-01  -2.8848e-16   6.4803e-16   9.9153e-02  -8.2175e-02]\n",
      " [  4.6782e-03   2.6677e-03  -2.7894e-01  -1.2061e-14  -6.0772e-15\n",
      "   -2.2505e+00   4.9578e-15  -8.2818e-15  -5.5206e-01  -4.3456e-01]\n",
      " [  7.2546e-20   1.1095e-16  -1.2815e-15   4.5318e-01  -3.8811e-01\n",
      "    4.9231e-16   9.1216e-01  -8.7003e-02   2.2031e-15   7.8711e-16]\n",
      " [ -1.1062e-16   1.1062e-16   1.1764e-15  -3.8811e-01  -4.5318e-01\n",
      "   -9.5030e-16   8.7003e-02   9.1216e-01   1.5582e-15   2.6686e-15]\n",
      " [ -5.0058e-03  -6.0813e-03  -4.6900e-01  -8.0938e-15  -3.5467e-15\n",
      "   -1.2816e+00   1.8651e-15  -3.7817e-15   4.9565e-01  -1.0808e+00]\n",
      " [ -7.0780e-01   7.0841e-01   1.5671e-01  -5.7129e-16  -1.6943e-16\n",
      "   -1.5457e-01   1.4097e-16  -1.0284e-15   9.9153e-02   8.2175e-02]\n",
      " [  4.6782e-03  -2.6677e-03  -2.7894e-01   1.1609e-14   4.7523e-15\n",
      "    2.2505e+00  -3.3062e-15   9.7330e-15  -5.5206e-01   4.3456e-01]\n",
      " [ -1.7935e-19   1.7925e-19  -1.2548e-16   4.5318e-01  -3.8811e-01\n",
      "   -3.4130e-15  -9.1216e-01   8.7003e-02  -8.8347e-16  -2.9908e-16]\n",
      " [ -9.8467e-19   9.8600e-19   1.1081e-16  -3.8811e-01  -4.5318e-01\n",
      "    6.7411e-15  -8.7003e-02  -9.1216e-01  -6.2712e-16  -9.7080e-16]\n",
      " [  5.0058e-03  -6.0813e-03   4.6900e-01  -6.4927e-15  -4.7761e-15\n",
      "   -1.2816e+00   4.1235e-15  -3.2508e-15  -4.9565e-01  -1.0808e+00]]\n",
      "Shell array: [0, 0, 0, 0, 0, 0, 0]\n",
      "****\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 1 :   -102.8434    28.8108  -101.1222    70.5903\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 2 :   -107.1513    28.8108  -100.4065    64.8509\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 3 :   -107.1860    28.8108  -100.3656    64.7345\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 4 :   -107.1871    28.8108  -100.3584    64.7189\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 5 :   -107.1872    28.8108  -100.3571    64.7162\n",
      "Energy converged\n",
      "----GVB calculation on N2 at R=0.950\n",
      "**** PyQuante GVB ****\n",
      "Stoichiometry = N2, Charge = 0, Multiplicity = 1\n",
      "7 N     0.000000     0.000000    -0.897620\n",
      "7 N     0.000000     0.000000     0.897620\n",
      "Nuclear repulsion energy: 27.294\n",
      "Basis set: sto3g\n",
      "  ncore/open/pair: 7,0,0\n",
      "  occ/bf/orb: 7,10,10\n",
      "Shell 0\n",
      "  occupation = 1.00\n",
      "  orbitals in shell [0, 1, 2, 3, 4, 5, 6]\n",
      "  couplings to other shells [(2.0, -1.0)]\n",
      "Starting guess at orbitals:\n",
      "[[ -7.0792e-01  -7.0842e-01   1.4746e-01  -1.4191e-14  -1.6416e-14\n",
      "   -1.6334e-01   5.4852e-16  -3.1084e-16  -1.0269e-01   8.3468e-02]\n",
      " [  4.6289e-03   5.0286e-03  -2.8983e-01   1.7109e-13   1.9876e-13\n",
      "    1.9731e+00  -5.7751e-15   3.2758e-15   5.5178e-01   1.9018e-01]\n",
      " [  1.1070e-16  -1.1070e-16   1.2359e-15   6.0305e-01  -3.5695e-02\n",
      "   -4.6554e-14   7.7309e-01   4.4279e-01  -5.8961e-16  -1.8708e-15]\n",
      " [ -1.1072e-16   1.1072e-16   2.4263e-16   3.5695e-02   6.0305e-01\n",
      "   -6.1357e-14   4.4279e-01  -7.7309e-01   7.5753e-16  -6.7561e-16]\n",
      " [ -4.5096e-03  -3.9955e-03  -4.5612e-01   1.0517e-13   1.2163e-13\n",
      "    1.2010e+00  -2.1554e-15   1.6720e-15  -4.9228e-01   9.5328e-01]\n",
      " [ -7.0792e-01   7.0842e-01   1.4746e-01   1.3764e-14   1.6340e-14\n",
      "    1.6334e-01  -7.8501e-16   1.7889e-16  -1.0269e-01  -8.3468e-02]\n",
      " [  4.6289e-03  -5.0286e-03  -2.8983e-01  -1.7026e-13  -1.9911e-13\n",
      "   -1.9731e+00   6.1600e-15  -2.3547e-15   5.5178e-01  -1.9018e-01]\n",
      " [  8.6324e-19  -8.6350e-19   1.1032e-16   6.0305e-01  -3.5695e-02\n",
      "   -5.0305e-14  -7.7309e-01  -4.4279e-01   2.2448e-16   6.3022e-16]\n",
      " [ -8.1411e-19   8.1466e-19   1.9389e-17   3.5695e-02   6.0305e-01\n",
      "   -6.6404e-14  -4.4279e-01   7.7309e-01  -2.4696e-16   2.5062e-16]\n",
      " [  4.5096e-03  -3.9955e-03   4.5612e-01   1.0447e-13   1.2110e-13\n",
      "    1.2010e+00  -2.6543e-15   2.2067e-15   4.9228e-01   9.5328e-01]]\n",
      "Shell array: [0, 0, 0, 0, 0, 0, 0]\n",
      "****\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 1 :   -103.1909    27.2944  -100.1315    69.7777\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 2 :   -107.2969    27.2944   -99.2646    63.9379\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 3 :   -107.3275    27.2944   -99.2145    63.8071\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 4 :   -107.3287    27.2944   -99.2053    63.7875\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 5 :   -107.3287    27.2944   -99.2035    63.7838\n",
      "Energy converged\n",
      "----GVB calculation on N2 at R=0.975\n",
      "**** PyQuante GVB ****\n",
      "Stoichiometry = N2, Charge = 0, Multiplicity = 1\n",
      "7 N     0.000000     0.000000    -0.921241\n",
      "7 N     0.000000     0.000000     0.921241\n",
      "Nuclear repulsion energy: 26.595\n",
      "Basis set: sto3g\n",
      "  ncore/open/pair: 7,0,0\n",
      "  occ/bf/orb: 7,10,10\n",
      "Shell 0\n",
      "  occupation = 1.00\n",
      "  orbitals in shell [0, 1, 2, 3, 4, 5, 6]\n",
      "  couplings to other shells [(2.0, -1.0)]\n",
      "Starting guess at orbitals:\n",
      "[[ -7.0796e-01  -7.0843e-01   1.4344e-01  -1.6657e-01   1.9309e-15\n",
      "    3.1243e-15   3.0476e-16   5.7318e-16  -1.0423e-01   8.3778e-02]\n",
      " [  4.6099e-03   5.7457e-03  -2.9542e-01   1.8518e+00  -2.0888e-14\n",
      "   -3.4206e-14  -3.0020e-15  -5.4937e-15   5.5148e-01   1.0730e-01]\n",
      " [  5.5345e-17  -5.5345e-17  -2.1587e-16  -1.2675e-14   5.4288e-02\n",
      "   -6.0534e-01   1.8441e-01  -8.5996e-01  -8.2067e-16   7.5287e-16]\n",
      " [  1.1079e-16  -1.1079e-16   2.4283e-16  -9.1680e-15  -6.0534e-01\n",
      "   -5.4288e-02  -8.5996e-01  -1.8441e-01  -1.6174e-15  -3.8065e-16]\n",
      " [ -4.2981e-03  -3.2550e-03  -4.5046e-01   1.1577e+00  -1.2682e-14\n",
      "   -2.0542e-14  -1.3841e-15  -2.2605e-15  -4.9103e-01   9.0874e-01]\n",
      " [ -7.0796e-01   7.0843e-01   1.4344e-01   1.6657e-01  -1.8245e-15\n",
      "   -3.1003e-15  -1.8157e-16  -4.2078e-16  -1.0423e-01  -8.3778e-02]\n",
      " [  4.6099e-03  -5.7457e-03  -2.9542e-01  -1.8518e+00   1.9882e-14\n",
      "    3.3730e-14   1.7208e-15   4.5653e-15   5.5148e-01  -1.0730e-01]\n",
      " [  4.7015e-19  -4.7044e-19  -1.7455e-17  -8.4236e-15   5.4288e-02\n",
      "   -6.0534e-01  -1.8441e-01   8.5996e-01   2.8002e-16  -2.4529e-16]\n",
      " [  6.6894e-19  -6.6925e-19   1.9717e-17  -6.0978e-15  -6.0534e-01\n",
      "   -5.4288e-02   8.5996e-01   1.8441e-01   5.7098e-16   1.3455e-16]\n",
      " [  4.2981e-03  -3.2550e-03   4.5046e-01   1.1577e+00  -1.3193e-14\n",
      "   -2.1221e-14  -2.3169e-15  -3.2516e-15   4.9103e-01   9.0874e-01]]\n",
      "Shell array: [0, 0, 0, 0, 0, 0, 0]\n",
      "****\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 1 :   -103.3672    26.5945   -99.6478    69.3338\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 2 :   -107.3489    26.5945   -98.7230    63.5026\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 3 :   -107.3784    26.5945   -98.6689    63.3647\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 4 :   -107.3796    26.5945   -98.6586    63.3432\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 5 :   -107.3796    26.5945   -98.6566    63.3390\n",
      "Energy converged\n",
      "----GVB calculation on N2 at R=1.000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**** PyQuante GVB ****\n",
      "Stoichiometry = N2, Charge = 0, Multiplicity = 1\n",
      "7 N     0.000000     0.000000    -0.944863\n",
      "7 N     0.000000     0.000000     0.944863\n",
      "Nuclear repulsion energy: 25.930\n",
      "Basis set: sto3g\n",
      "  ncore/open/pair: 7,0,0\n",
      "  occ/bf/orb: 7,10,10\n",
      "Shell 0\n",
      "  occupation = 1.00\n",
      "  orbitals in shell [0, 1, 2, 3, 4, 5, 6]\n",
      "  couplings to other shells [(2.0, -1.0)]\n",
      "Starting guess at orbitals:\n",
      "[[ -7.0800e-01  -7.0844e-01   1.3979e-01   1.6904e-01   1.9015e-17\n",
      "    1.1326e-15  -1.7358e-16  -3.2738e-16   1.0562e-01   8.4084e-02]\n",
      " [  4.5938e-03   6.2544e-03  -3.0109e-01  -1.7421e+00   3.3799e-16\n",
      "   -1.0662e-14   1.9260e-15   2.2259e-15  -5.5107e-01   3.9815e-02]\n",
      " [  5.5314e-17  -8.3070e-17   3.5906e-16   7.6425e-16  -6.0639e-01\n",
      "   -7.7910e-02  -7.4895e-01   4.4045e-01  -2.0889e-15   2.0307e-15]\n",
      " [ -1.1062e-16   1.1062e-16  -8.0195e-16  -4.9790e-15  -7.7910e-02\n",
      "    6.0639e-01  -4.4045e-01  -7.4895e-01  -4.4863e-16  -7.8179e-16]\n",
      " [ -4.1067e-03  -2.6616e-03  -4.4526e-01  -1.1156e+00   1.5088e-16\n",
      "   -6.3482e-15   8.1958e-16   8.7970e-16   4.9005e-01   8.7179e-01]\n",
      " [ -7.0800e-01   7.0844e-01   1.3979e-01  -1.6904e-01  -3.0582e-16\n",
      "   -1.1221e-15  -6.6151e-17   4.8962e-16   1.0562e-01  -8.4084e-02]\n",
      " [  4.5938e-03  -6.2544e-03  -3.0109e-01   1.7421e+00   9.2334e-16\n",
      "    1.0338e-14  -1.9200e-16  -2.6326e-15  -5.5107e-01  -3.9815e-02]\n",
      " [  5.8238e-19  -5.8255e-19   5.2079e-18   3.6701e-16  -6.0639e-01\n",
      "   -7.7910e-02   7.4895e-01  -4.4045e-01   6.7912e-16  -5.8893e-16]\n",
      " [ -1.1816e-18   1.1820e-18  -6.7843e-17  -2.3517e-15  -7.7910e-02\n",
      "    6.0639e-01   4.4045e-01   7.4895e-01   1.3675e-16   2.1920e-16]\n",
      " [  4.1067e-03  -2.6616e-03   4.4526e-01  -1.1156e+00   1.4372e-15\n",
      "   -6.1000e-15   2.3664e-15   1.3825e-16  -4.9005e-01   8.7179e-01]]\n",
      "Shell array: [0, 0, 0, 0, 0, 0, 0]\n",
      "****\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 1 :   -103.5355    25.9297   -99.1722    68.8793\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 2 :   -107.3896    25.9297   -98.2002    63.0811\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 3 :   -107.4183    25.9297   -98.1421    62.9363\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 4 :   -107.4195    25.9297   -98.1310    62.9127\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 5 :   -107.4195    25.9297   -98.1287    62.9081\n",
      "Energy converged\n",
      "----GVB calculation on N2 at R=1.025\n",
      "**** PyQuante GVB ****\n",
      "Stoichiometry = N2, Charge = 0, Multiplicity = 1\n",
      "7 N     0.000000     0.000000    -0.968485\n",
      "7 N     0.000000     0.000000     0.968485\n",
      "Nuclear repulsion energy: 25.297\n",
      "Basis set: sto3g\n",
      "  ncore/open/pair: 7,0,0\n",
      "  occ/bf/orb: 7,10,10\n",
      "Shell 0\n",
      "  occupation = 1.00\n",
      "  orbitals in shell [0, 1, 2, 3, 4, 5, 6]\n",
      "  couplings to other shells [(2.0, -1.0)]\n",
      "Starting guess at orbitals:\n",
      "[[ -7.0803e-01  -7.0846e-01  -1.3649e-01  -1.7082e-01   4.4845e-16\n",
      "   -5.0564e-16  -1.5956e-16  -1.1915e-16  -1.0686e-01   8.4432e-02]\n",
      " [  4.5803e-03   6.6068e-03   3.0683e-01   1.6432e+00  -2.1644e-15\n",
      "    5.4668e-15  -2.6107e-16   2.1615e-15   5.5055e-01  -1.6313e-02]\n",
      " [  5.5483e-17  -2.7727e-17   6.1174e-16  -4.2570e-15  -3.5057e-01\n",
      "    5.0521e-01   7.9129e-01   3.3408e-01   5.2696e-15   5.7975e-15]\n",
      " [  1.1069e-16  -1.1069e-16  -1.3863e-15   2.0331e-16  -5.0521e-01\n",
      "   -3.5057e-01   3.3408e-01  -7.9129e-01   4.7223e-15   4.5414e-15]\n",
      " [ -3.9327e-03  -2.1854e-03   4.4047e-01   1.0754e+00  -1.2374e-15\n",
      "    2.8728e-15  -3.2905e-16   1.0834e-15  -4.8932e-01   8.4048e-01]\n",
      " [ -7.0803e-01   7.0846e-01  -1.3649e-01   1.7082e-01  -1.1405e-15\n",
      "    8.1610e-16   1.3531e-15   5.5628e-17  -1.0686e-01  -8.4432e-02]\n",
      " [  4.5803e-03  -6.6068e-03   3.0683e-01  -1.6432e+00   6.0930e-15\n",
      "   -6.0818e-15  -5.9749e-15  -1.1505e-15   5.5055e-01   1.6313e-02]\n",
      " [  8.7380e-20  -8.7492e-20  -3.4775e-18  -9.1525e-16  -3.5057e-01\n",
      "    5.0521e-01  -7.9129e-01  -3.3408e-01  -2.5715e-15  -2.3149e-15]\n",
      " [  1.0252e-18  -1.0254e-18  -6.9275e-17  -2.8638e-16  -5.0521e-01\n",
      "   -3.5057e-01  -3.3408e-01   7.9129e-01  -9.1642e-16  -8.2853e-16]\n",
      " [  3.9327e-03  -2.1854e-03  -4.4047e-01   1.0754e+00   3.1163e-15\n",
      "    3.7861e-15  -5.7217e-15   3.0303e-15   4.8932e-01   8.4048e-01]]\n",
      "Shell array: [0, 0, 0, 0, 0, 0, 0]\n",
      "****\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 1 :   -103.6938    25.2973   -98.7053    68.4195\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 2 :   -107.4205    25.2973   -97.6954    62.6731\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 3 :   -107.4487    25.2973   -97.6337    62.5214\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 4 :   -107.4499    25.2973   -97.6215    62.4958\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 5 :   -107.4500    25.2973   -97.6190    62.4907\n",
      "Energy converged\n",
      "----GVB calculation on N2 at R=1.050\n",
      "**** PyQuante GVB ****\n",
      "Stoichiometry = N2, Charge = 0, Multiplicity = 1\n",
      "7 N     0.000000     0.000000    -0.992106\n",
      "7 N     0.000000     0.000000     0.992106\n",
      "Nuclear repulsion energy: 24.695\n",
      "Basis set: sto3g\n",
      "  ncore/open/pair: 7,0,0\n",
      "  occ/bf/orb: 7,10,10\n",
      "Shell 0\n",
      "  occupation = 1.00\n",
      "  orbitals in shell [0, 1, 2, 3, 4, 5, 6]\n",
      "  couplings to other shells [(2.0, -1.0)]\n",
      "Starting guess at orbitals:\n",
      "[[ -7.0805e-01  -7.0847e-01  -1.3351e-01  -1.7201e-01   2.9140e-16\n",
      "    4.2693e-16   2.5489e-16  -6.8555e-17  -1.0797e-01   8.4834e-02]\n",
      " [  4.5688e-03   6.8417e-03   3.1265e-01   1.5539e+00  -2.0457e-15\n",
      "   -3.0922e-15  -1.4228e-15   2.8368e-16   5.4991e-01  -6.3705e-02]\n",
      " [  5.5368e-17  -5.5368e-17   3.2293e-16   2.3474e-15   2.2425e-01\n",
      "    5.7633e-01   8.4624e-01  -7.5974e-02  -1.4730e-16  -1.2673e-15]\n",
      " [  1.1083e-16  -1.1083e-16  -2.6768e-17  -5.3716e-16  -5.7633e-01\n",
      "    2.2425e-01   7.5974e-02   8.4624e-01   5.8670e-16   8.1063e-16]\n",
      " [ -3.7735e-03  -1.8033e-03   4.3607e-01   1.0376e+00  -1.0760e-15\n",
      "   -1.5162e-15  -2.1227e-16   2.1156e-17  -4.8880e-01   8.1355e-01]\n",
      " [ -7.0805e-01   7.0847e-01  -1.3351e-01   1.7201e-01  -1.1065e-16\n",
      "   -2.9567e-16  -1.7282e-16   1.2987e-16  -1.0797e-01  -8.4834e-02]\n",
      " [  4.5688e-03  -6.8417e-03   3.1265e-01  -1.5539e+00   2.1306e-15\n",
      "    2.9940e-15   1.3455e-15  -5.9383e-16   5.4991e-01   6.3705e-02]\n",
      " [  4.6446e-19  -4.6442e-19   3.2972e-17   6.7046e-16   2.2425e-01\n",
      "    5.7633e-01  -8.4624e-01   7.5974e-02   2.7737e-17   3.3035e-16]\n",
      " [  6.3344e-19  -6.3353e-19  -3.2116e-18  -1.4870e-16  -5.7633e-01\n",
      "    2.2425e-01  -7.5974e-02  -8.4624e-01  -1.7050e-16  -1.9921e-16]\n",
      " [  3.7735e-03  -1.8033e-03  -4.3607e-01   1.0376e+00  -6.2270e-16\n",
      "   -1.2121e-15   5.8003e-17  -3.9228e-16   4.8880e-01   8.1355e-01]]\n",
      "Shell array: [0, 0, 0, 0, 0, 0, 0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 1 :   -103.8415    24.6949   -98.2473    67.9581\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 2 :   -106.6556    24.6949   -97.2626    63.1747\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 3 :   -106.6789    24.6949   -97.1272    62.8806\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 4 :   -106.6797    24.6949   -97.0996    62.8246\n",
      "Energy converged\n",
      "----GVB calculation on N2 at R=1.100\n",
      "**** PyQuante GVB ****\n",
      "Stoichiometry = N2, Charge = 0, Multiplicity = 1\n",
      "7 N     0.000000     0.000000    -1.039349\n",
      "7 N     0.000000     0.000000     1.039349\n",
      "Nuclear repulsion energy: 23.572\n",
      "Basis set: sto3g\n",
      "  ncore/open/pair: 7,0,0\n",
      "  occ/bf/orb: 7,10,10\n",
      "Shell 0\n",
      "  occupation = 1.00\n",
      "  orbitals in shell [0, 1, 2, 3, 4, 5, 6]\n",
      "  couplings to other shells [(2.0, -1.0)]\n",
      "Starting guess at orbitals:\n",
      "[[ -7.0809e-01  -7.0849e-01  -1.2846e-01  -1.7297e-01   4.3140e-16\n",
      "    9.8237e-18  -2.7890e-18  -2.2921e-16   1.0981e-01  -8.5786e-02]\n",
      " [  4.5509e-03   7.0695e-03   3.2449e-01   1.4007e+00  -2.8735e-15\n",
      "   -1.0612e-15   9.4433e-16   1.3446e-15  -5.4831e-01   1.3904e-01]\n",
      " [ -3.4698e-17   4.8575e-17   9.3089e-16   1.3418e-15   5.8743e-01\n",
      "   -2.1403e-01   3.4216e-01  -7.5930e-01   9.7060e-19   1.0015e-15]\n",
      " [ -5.3214e-20  -1.1097e-16  -4.3725e-16   6.9155e-16   2.1403e-01\n",
      "    5.8743e-01  -7.5930e-01  -3.4216e-01  -3.7971e-15  -3.3046e-15]\n",
      " [ -3.4921e-03  -1.2513e-03   4.2827e-01   9.6902e-01  -1.3584e-15\n",
      "   -6.4093e-16   5.2954e-16   2.5803e-16   4.8838e-01  -7.6963e-01]\n",
      " [ -7.0809e-01   7.0849e-01  -1.2846e-01   1.7297e-01  -7.2182e-17\n",
      "    2.4516e-16  -2.9967e-16  -1.4369e-18   1.0981e-01   8.5786e-02]\n",
      " [  4.5509e-03  -7.0695e-03   3.2449e-01  -1.4007e+00   1.0434e-15\n",
      "   -8.5138e-16   1.4879e-15  -2.9368e-16  -5.4831e-01  -1.3904e-01]\n",
      " [  1.1615e-20  -1.1528e-20   4.5843e-17   3.6346e-16   5.8743e-01\n",
      "   -2.1403e-01  -3.4216e-01   7.5930e-01  -5.0064e-16  -6.0467e-16]\n",
      " [  1.9064e-19  -1.9065e-19  -2.4483e-17   2.0725e-16   2.1403e-01\n",
      "    5.8743e-01   7.5930e-01   3.4216e-01   6.2705e-16   5.0335e-16]\n",
      " [  3.4921e-03  -1.2513e-03  -4.2827e-01   9.6902e-01  -2.1401e-15\n",
      "   -3.3234e-15   3.4648e-15   7.7126e-16  -4.8838e-01  -7.6963e-01]]\n",
      "Shell array: [0, 0, 0, 0, 0, 0, 0]\n",
      "****\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 1 :   -104.1058    23.5724   -97.3587    67.0392\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 2 :   -106.7461    23.5724   -96.3295    62.3404\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 3 :   -106.7689    23.5724   -96.1911    62.0409\n",
      "ROTION Delta Matrix\n",
      "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.  0.  0.]]\n",
      "---- 4 :   -106.7696    23.5724   -96.1638    61.9856\n",
      "Energy converged\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1191f5250>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+684z3oWZfcjMPMBO4Avnq12Y2Z1mVm5m5Y2NjQMQemQaOzqOr1xbzKt3L+Lu\nj01j19FWPvUfr3PzT19lfVW9hh2RD/B4W0gdFUvemFHhDkWGiF4ThpmtM7OKHj7Lgjz3CuDR8210\nzr3unJsOXA7cbWY9dvdxzq1yzpU558oyMzODDCnyJcXH8H8+MpnNdy3kO8umU9/Szh2/Kudj/28T\nq7d76dKwI+Ln8bYwIy9FNVA5q9eE4Zxb7Jyb0cPn6QscVgsUBCzn+9cBYGazgBjn3NY+nL8KaANm\n9Lav9F1CbDS3XzGBjX+3gB/cMouOrm7+5tG3WfSDjfz2zcOc7tSwIyNZR1c3u4+26oU9eZ9QPZJa\nDawws3gzmwgUAW8EbL+NC9QuzGyimcX4v48HpgEHQxTriBYbHcXNl+Xzwt9+hJ99eg5JCTHc9fud\nfOR7G3h48wFOnFY/g5Fob30bp7u61X4h7xNst9rlZlYDXAGsNbPnAZxzHuBxoBJ4DljpnAsc8OhW\nzkkYZnajmX3bvzgfX8+obcBTwF8555qCiVUuLCrKWDIjhzVfnM+vPjeXgrREvv1MJfPv28ADG6pp\nPtkR7hBlEHm8avCWD7JIauwsKytz5eXl4Q4jYrx58B0e2FDNxt2NJMfHcPsV4/nc/Il663cEuHe1\nh9++eYSKf7yO6Ci1YUQ6M9vqnCvrbT+96S3ndfmENH752blU1Dbz0437+OlL+/j55gPc5h92RL1n\nIlelt4WSnGQlC3kfJQzp1Yy8VB740znsa2zjZxv38V+vHeK/XjvEgqlZLJ2Vw+KSbEbH61aKFN3d\nDo+3mZvm5Ic7FBli9L9c+mxyZhLfu2UWX762mF++coA12+tYV1VPQmwUi6Zls3RWDgumZpEQGx3u\nUCUIh945wfHTXczIU/uFvJ8Shly0vDGjuOdPSrn7YyWUH3qXNdu9/O/OOtburCMpPoaPlmazdFYu\nV07JIC5GU64MN39s8FaXWnk/JQzpt6goY+7ENOZOTONbS0vZsv8Yz2yv49mKOp58u5bUUbF8bMY4\nls7KZd6kdD0PHyYqaluIiTKKspPCHYoMMUoYMiBioqO4qiiTq4oy+c7HZ7BpbyNrtntZs93LY28e\nISMpnj+5xJc85hSOJUrJY8jyeJspyk4mPkaPFuX9lDBkwMXFRLGoJJtFJdmcPN3Fht0NZxPHr7Yc\nIjc1gRtm5bJ0Zq6GnhhinHNUelu4ZlpWuEORIUgJQ0JqVFw011+Sw/WX5NDW3sm6ynrWbPfyi1cO\nsOrl/YyFQErmAAAMfElEQVRPT2TpzFyWzspl6rjkcIc74tW3tHPs+Gm9sCc9UsKQQZMUH8PHZ+fx\n8dl5vHfiNM97jvLMjjoe3FjNTzZUU5ydxNKZudwwK5eJGaPDHe6IVHFmSHNNmiQ9UMKQsBiTGMcn\nLy/kk5cX0tjaznMVdazZXscPXtjDD17YwyV5qdwwM4cbZuXqBcFB5PG2YAYlOaphyAcpYUjYZSbH\nc/sVE7j9ignUNZ9k7Y461uyo41+e3cW/PLuLy8aPZenMHK6fmUNWco+j3MsA8XibmZg+miS9iCk9\n0FhSMmQdPnaCNTt8Pa12HW0lymDepHSWzsplyfRxjB0dF+4QI86V//oiswvH8JNPzQl3KDKI+jqW\nlBKGDAt761tZs6OOZ7Z72d90nJgoY35RBktn5nLt9GxSEmLDHeKw9+7x08z+zgvctWQaf7lgcrjD\nkUGkwQclohRlJ/OVa5P528VFVNa1sGZ7HWu2e/nqE9uJeyqKhVMzWTorl2umZZEYp9u6PyrrWgAN\naS7np/9ZMqyYGdNzU5mem8pdS6by9pH3WLPdy9oddTzvqWdUbDRXTknnyikZXFWUweTMJL3n0Uea\nA0N6o4Qhw5aZMadwLHMKx/L3f1LKmwffYe2OOl7e28i6qgYAclITziaPK6dkaC6PC6iobSEnNYF0\nXSM5j6AShpndAtwLlABznXPlAdvuBu4AuoC/cc49b2bJwKaAIvKB/3LOffk85Rfim7XvXufc94OJ\nVSJbdJQxb1I68yalA3DknRNs2tvE5upGXqis53dbawBfd9GrijKYPyWDuRPTNLJuAI+3WbULuaBg\naxgVwE3AQ4ErzawUWAFMB3KBdWZW7JxrBS4N2G8r8OQFyv8h8GyQMcoIVJCWyKc+VMinPlRIl39+\nh017m9i8t4lfvnKQVS/vJy4missnjGX+lEyuKsqgNCdlxI5xdeJ0J/ubjnPDzNxwhyJDWFAJwzlX\nBfT0jHgZ8Jhzrh04YGbVwFxgy5kdzKwYyOL9NQ4Ctn8cOAAcDyZGkegoY2b+GGbmj2HlwimcON3J\nGwfeYfPeJjZXN3Hfc7u47zkYmxjLh6dkcNWUDOYXZZA/NjHcoQ+aqrpWnFP7hVxYqNow8oDXApZr\n/OsCrQB+63ro12tmScBdwLXA10IUo4xQiXExLJiaxYKpvgH2GlpP8Up109kayNoddQBMzBjNfH/y\nuGJyekR33T3b4K0hQeQCek0YZrYOGNfDpnucc08Hce4VwO3n2XYv8O/OubbeeriY2Z3AnQCFhYVB\nhCMjVVZyAstn57N8dj7OOfY2tPmTRyO/f6uG37x2iOgoY1Z+KvOLfI+vLi0YQ2x05EwO5altYUxi\nLLmpepNezq/XhOGcW9yPcmuBgoDlfP86AMxsFhDjnNt6nuM/BHzCzP4NGAN0m9kp59xPeohvFbAK\nfC/u9SNWkbPMjOLsZIqzk7lj/kROd3bz1uF32by3iU3VTfzkxb38aP1ekuJjmDcp7WwNZLh33/XU\nNTMjN3VY/x0k9EL1SGo18N9m9kN8jd5FwBsB228DHj3fwc65q858N7N7gbaekoVIqMXFRJ3tffW1\n66bSfKKDLfv9j6+qmyKi++7pzm72HG3js1dOCHcoMsQF2612OfBjIBNYa2bbnHPXOec8ZvY4vi6x\nncBK51xXwKG3AtefU9aNQJlz7pvBxCQSSqmJsSyZkcOSGTlAZHTf3dvQyumubkrV4C290FhSIgOk\nq9tRUdvM5uomNu1tZOuhd+nocme77142Po2ZeanMLEgdUqPuPl5+hK//bgfrvvIRpmRpHu+RSGNJ\niQyy6ChjVsEYZhX8sfvu6/7uu6/42z+6/b+f5aQmcEleKrMKxnBJXioz81MZkxie0XcrvS0kxkVr\n0irplRKGSIgkxsWwcGoWC/3dd4+3d1JZ18L2I++xs7aZHTXN/KGy/uz+hWmJzMxP9X/GMCMvdVDm\npaiobaYkJ4XoEfrSovSdEobIIBkdH8PlE9K4fELa2XXNJzqo8DazveY9dtY08/bh93jG/x6IGUzO\nTPIlkLxUZhaMoTQnZUDbQ7q7HVV1Ldx8Wf6AlSmRSwlDJIxSE2O5coqvZ9UZTW3t7Kzx1UB21LzH\ny3uaePItX6/0mChft98ztZCZ+alMHZfc73dCDh47zvHTXczI1Qt70jslDJEhJiMpnoXTslg4zfco\nyznH0ZZTbD/SzM7a99hR08yzFUd57M0jgK/rb2lOyvuSyOTMpD49YvJ4fXNgqIeU9IUShsgQZ2bk\npI4iJ3UUS2b4Bl1wznH4nRNsr2lmZ817bK9p5ndba/j1lkMAJMZFMyPX3x5SMIaZeamMT0/8wIt5\nFd5mYqN9tRaR3ihhiAxDZsb49NGMTx/NjbN8I8x2dTv2N7adfZS1vaaZX792iNObDwCQkhBztgZy\npjZS6W2hKCuZuJjIGeZEQkcJQyRCREcZRdnJFGUnn23E7ujqZvfRVn+vLN/jrFUv76ez+4/vX91a\npgZv6RslDJEIFhsdxYy8VGbkpXLbXN/gnKc6uqiqa2FHTTO7jrZy29yCXkoR8VHCEBlhEmKjmV04\nltmFY8MdigwzenApIiJ9ooQhIiJ9ooQhIiJ9ooQhIiJ9ooQhIiJ9ooQhIiJ9ooQhIiJ9ooQhIiJ9\nElFTtJpZI3AoiCIygKYBCmcgKa6Lo7gujuK6OJEY13jnXGZvO0VUwgiWmZX3ZV7bwaa4Lo7iujiK\n6+KM5Lj0SEpERPpECUNERPpECeP9VoU7gPNQXBdHcV0cxXVxRmxcasMQEZE+UQ1DRET6JGIThpkt\nMbPdZlZtZt/oYftYM3vKzHaY2RtmNqO3Y80szcxeMLO9/j8vekKB/sZlZgVmtsHMKs3MY2ZfCjjm\nXjOrNbNt/s/1gxWXf9tBM9vpP3d5wPpwXq+pAddjm5m1mNmX/duCul5m9rCZNZhZxXm2m5n9yB/z\nDjOb09vfZyCuVTCxDcL9Fcw1C+X91d/rFbL7q49xTTOzLWbWbmZfO2db6O4x51zEfYBoYB8wCYgD\ntgOl5+zzPeBb/u/TgPW9HQv8G/AN//dvAPcNYlw5wBz/92RgT0Bc9wJfC8f18i8fBDJ6KDds16uH\nco7i62s+ENframAOUHGe7dcDzwIGzANeD/W9NQCxhez+CiauUN5fwcYVqvurj3FlAZcD3w08V6jv\nsUitYcwFqp1z+51zp4HHgGXn7FMKvAjgnNsFTDCz7F6OXQb8yv/9V8DHBysu51ydc+4t//pWoArI\nu8jzD3hcvZQbtut1zj6LgH3OuWBe6jzLOfcy8M4FdlkG/Nr5vAaMMbMcQntvBRVbiO+vYK7ZhQR9\nzQYorgG9v/oSl3OuwTn3JtBxzqaQ3mORmjDygCMByzV88ObfDtwEYGZzgfFAfi/HZjvn6vzfjwK9\n/cAcyLjOMrMJwGzg9YDVf+2vMj/cj6pmsHE5YJ2ZbTWzOwOOGRLXC1gBPHrOumCuV2/OF3co761g\nYzsrBPdXsHGF6v4KNq4zBvv+upCQ3mORmjD64l/x/bawDfhr4G2gq68HO1+9LhRdzC4Yl5klAb8H\nvuyca/Gv/im+KuilQB3wg0GOa75z7lLgY8BKM7v63IPDeL3igBuBJwKOGYzr1W8hvFa9CuP9dSHh\nvL8uaDjeX9D/6xUTgliGglqgIGA537/uLP9/hs+Cr2ELOADsB0Zd4Nj6M9V3f7W0YRDjwsxi8f1n\nfsQ592TAMfVnvpvZfwDPDGZczrla/58NZvYUvmrxy4T5evl9DHgr8BoNwPXqb9yx51kPwV+rYGML\n5f0VVFwhvL+CissvHPfXhVwo3qCvV6TWMN4Eisxsov83gBXA6sAdzGyMfxvAXwAv+3/4XOjY1cBn\n/N8/Azw9WHH5fxj+HKhyzv3wnGMCn6kuB3rsWRGiuEabWbJ/n9HARwPOH7brFbDLbZzzuGAArldv\nVgN/Zj7zgGb/o4BQ3ltBxRbi+yuYuEJ5f/U7roDt4bi/LiS099jFtpIPlw++3g178PUYuMe/7gvA\nF/zfr/Bv3w08CYy90LH+9enAemAvsA5IG6y4gPn4qpA7gG3+z/X+bb8Bdvq3rQZyBjGuSfjaEbYD\nnqFyvfzbRgPHgNRzygzqeuH7AVGHr8GxBrjjnJgMeMAf806gbDDurWBiG4T7q79xhfr+CubfMiT3\nVx/jGudf3wK85/+eEup7TG96i4hIn0TqIykRERlgShgiItInShgiItInShgiItInShgiItInShgi\nItInShgiItInShgiItIn/x+Trg0AqPx99wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118e91c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Egvbs = []\n",
    "for R in Rs:\n",
    "    n2 = n2_mol(R)\n",
    "    print \"----GVB calculation on N2 at R=%.3f\" % R\n",
    "    E = gvb(n2,verbose=True)\n",
    "    Egvbs.append(E)\n",
    "plt.plot(Rs,Egvbs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# R     Etot        Enuke     Eone        Etwo\n",
    "data = \"\"\"\\\n",
    "0.900  -107.1872    28.8108  -100.3571    64.7162\n",
    "0.950  -107.3287    27.2944   -99.2035    63.7838\n",
    "0.975  -107.3796    26.5945   -98.6566    63.3390\n",
    "1.000  -107.4195    25.9297   -98.1287    62.9081\n",
    "1.025  -107.4500    25.2973   -97.6190    62.4907\n",
    "1.050  -106.6797    24.6949   -97.0996    62.8246\n",
    "1.100  -106.7696    23.5724   -96.1638    61.9856\"\"\"\n",
    "Edata = []\n",
    "for line in data.splitlines():\n",
    "    Edata.append(map(float,line.split()))\n",
    "Edata = np.array(Edata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0.9   , -107.1872,   28.8108, -100.3571,   64.7162],\n",
       "       [   0.95  , -107.3287,   27.2944,  -99.2035,   63.7838],\n",
       "       [   0.975 , -107.3796,   26.5945,  -98.6566,   63.339 ],\n",
       "       [   1.    , -107.4195,   25.9297,  -98.1287,   62.9081],\n",
       "       [   1.025 , -107.45  ,   25.2973,  -97.619 ,   62.4907],\n",
       "       [   1.05  , -106.6797,   24.6949,  -97.0996,   62.8246],\n",
       "       [   1.1   , -106.7696,   23.5724,  -96.1638,   61.9856]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Edata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x782ea20>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GPHaSbfcA93Swfjsws4P1iwjXhHD3g4S7OItICnJ3AsEQV589LN6hyBnQTwIRSRo79x+h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\nHLypZzN8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x78194a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Edata[:,0],Edata[:,1])\n",
    "plt.title(\"Total Energy\")\n",
    "plt.ylabel(\"E/h\")\n",
    "plt.xlabel(\"R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x79dc400>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ddSYu2MqlL89k7NyN6i4KcQoCkRATlT+CJ7rV4+v+cTQoX5QnJy+nx+uJzN+k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\nEZEQpyAQEQlxCgIRkRD3fxr3QR5RUB1dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x871b860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Edata[:,0],Edata[:,2])\n",
    "plt.title(\"Nuclear Repulsion Energy\")\n",
    "plt.ylabel(\"E/h\")\n",
    "plt.xlabel(\"R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x873bcc0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UeC4ih0shIv9lU2YWt/9nDl8t26rGcxEpkUJEfvRh0Hi+PyeXP17ckyv7q/Fc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\nISIiIlH7f5VCp7nWCgRdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8941f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Edata[:,0],Edata[:,3])\n",
    "plt.title(\"One Electron Energy\")\n",
    "plt.ylabel(\"E/h\")\n",
    "plt.xlabel(\"R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x8a4e668>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pz6VEXByvDurCI33bnLJYQOQrcUf178jvLmiuT5WFTC0MkRh1+Nhx7pm6hCnz\nN/M/P6nJ41e3pUKpU7+5xiJ3Z+qCzTzwznL2HU5j8LlNGHpeU0qVOP1Wwqcrd/C/ry4gPs4Y0a8D\nPZqpXyOvdEtKpIhwd16ctZG/vbuCBlXKMHpAR5rVLB92rBz7dtdB7n1zCV+s2UmHBpV4uE8bmtfK\nW/4NOw8w6KVk1qXu5+6LWnDrT89Qv0YeqGCIFDFzN3zHkAnzOHT0OP+8qi0XnlU77EinlHb8BM99\nuYF/fbyahLg47rqwOdd1bkhcXP68se8/ksbv3ljE+0u3cWnbOvy9b2vKlCz6nyoLggqGSBG0bc9h\nbn95Hgs37WbIuU24s2dz4vPpDTg/LUnZw+8nL2b51r38omVN/tq7FbUrls7313F3Rn2+jsc+WEXz\nmuUZMyCJBlXL5PvrFHUqGCJF1JG049z/1jImzt3EOWdW56lr2lGpTMmwYwGRsSSPf7ia52duoFq5\nRP7auxUXtPrxR2Xz22erdvC/ExdgZvy7X3vOObN6oK9X1KhgiBRxE+d+y5+nLaNmxURGX5dEyzoV\nQs3z2aod3Dt1KZt3H+Lazg34/YUtqFi64Drov9l1gEEvzWPNjn3cdWELbjtH/Ro5pY/VihRx/To1\n4NXbunA07QR9Rs1k2sLNoeTYuf8Id7y6gBvHfU2pEnG8cXtXHrq8dYEWC4CGVcsyZUg3Lmpdm0fe\nX8mwiQs4eDStQDMUB2phiBRiO/YdZtiEBczd+B0DezTm7otakBAf/N+B7s6keSk8+N4KDhxJY+h5\nTRl8bhMSE8IdUOfujJ6xnkenr+TMmuUZPaAjDasWzdHy+UW3pESKkWPHT/Dguyt4YdZGup5RlRHX\ntqdqucTAXm/jzgPcM3UJs9btIqlhZR7p25qmNWLro74zVqcyfOIC3J2n+rXn3OY1wo4Us1QwRIqh\nyfNSuGfqEqqWLcnoAUm0rlcxX5//2PETPPvFep78eA0l4+O4++IW9Du7Qb59VDa/fbvrIIPGJ7Nq\n+z5+27M5Q85ton6NTKgPQ6QY6tuxHpMHd8PM6PvMLN5I3pRvz71o0256jZjJo9NXcV7zGnx858/o\nn4/jKoLQoGoZpgzpxiVt6vDYB6sY+sp8DhxRv0ZeqIUhUsR8d+AowyfOZ+baXQzo0pD7LmlJyYTc\n/W144Ega//hwFS/O2kiN8qX4S/SjsoVJ+lmAm9Yox+gBRXcW4NzQLSmRYi7t+Ake/WAVY2asJ6lh\nZUZe14Gir/0JAAAOP0lEQVQa5Uud1nN8unIHf3xzKVv2HGJAl4b87oLmlC+Ec1md9OWanQybOJ/j\nJyL9GuepXwNQwQg7hkjMeHvRFu6atJjypRIYdV1HOjasnO0xqfuO8Je3l/HO4q2cWbMcD/dpTceG\nVQogbfA2fXeQ28bPY8W2vdz5izMZel7TYt+voYIhIj9YuW0vg16ax9Y9h7i/Vyuu7dQg0zdJd+f1\n5E08+O4KDh87wfCfN+W2nzXJ9e2sWHXo6HHunrKYaQu3cGGrWvzjqraUKwbfbpiVmCkYZlYJGAuc\nBThwM9AHuBQ4CqwDbnL33ZkcuxHYBxwH0nLyC6lgiGRuz8Fj3PHaAj5blcrVSfX5S+9W/zW9+PrU\n/dwzdQmz139Hp8ZVeLhPa5pULxdi4mC5O899uYGH3lvBGdXLMWZAR84owr/vqcRSwXgR+MLdx5pZ\nSaAM0An4j7unmdnfAdz995kcuxFIcvedOX09FQyRrB0/4Tzx8Wr+/Z+1tK1fiVH9O1CtXCJjZqzj\nqf+spVRCHPdc/BOuSqof059+yk+z1u5k6CvzSTvuPHFN0ft2w5yIiYJhZhWBhcAZnsWLmNnlwBXu\n3j+TbRtRwRDJdx8s28adry8iMSGOauUSWbV9H79sU5s/X9rytDvGi4KU7yP9Gsu37uXX/3Mmw85r\nWmwKJsTOOIzGQCowzswWmNlYM8v4WbabgfezON6Bj81snpkNyupFzGyQmSWbWXJqamr+JBcpwi5o\nVYs3h3anUpkS7D18jLHXJ/H0taf/Kaqiol7lMkwe3I3L2tXl8Y9Wc9vL89h3+FjYsWJSkC2MJGA2\n0N3d55jZk8Bed78vuv1eIAnok1kLxMzquvtmM6sBfAQMd/cZp3pNtTBEci7t+AkcKFEAc08VBu7O\nuJkbefC9FTSqWoYx1ycV6X6ck2KlhZECpLj7nOjyJKADgJndCFwC9M/qdpW7b47+uwOYSqTvQ0Ty\nSUJ8nIpFOmbGzT0a8/Itnfn+4DEuGzGTj5ZvDztWTAnsanH3bcAmM2seXXU+sNzMLgTuAnq5+8HM\njjWzsmZW/uRjoCewNKisIiIndW1SlbeH96BRtbLc+lIyT3y8mhMnis7wg7wI+s+L4cAEM1sMtAMe\nAkYA5YGPzGyhmT0DYGZ1zOy96HE1gS/NbBEwF3jX3acHnFVEBIC6lUrzxu1d6dOhLk98vIZB45PZ\nq34NDdwTEcmKu/PirI088O4KGlYtw5gBSTStUbT6NWKlD0NEpFAzM27s3pgJAzuz5+AxLnt6Jh8s\n2xZ2rNCoYIiIZKPLGZF+jSbVy3Lb+Hk8/uGqYtmvoYIhIpIDdSqV5rXbunJFx3o89Z+1DHwpmT2H\nile/hgqGiEgOlSoRz2NXtOGB3q2YsTqVy56eyZrt+8KOVWBUMEREToOZMaBrI165tQv7Dkf6NaYv\n3Rp2rAKhgiEikgudGlfh7eE9aFqzPLe/PJ9/fLCK40W8X0MFQ0Qkl2pXLM3rt3Xh6qT6jPh0Lbe8\n+HWR7tdQwRARyYPEhHge6duav112FjPX7qT3iC9Zta1o9muoYIiI5JGZcV2Xhky8tQsHjh7n8pEz\neW9J0evXUMEQEcknSY2q8M7wHjSvVZ4hE+bz9+kri1S/hgqGiEg+qlmhFK8O6kK/TvUZ9dk6bnrh\na3YfPBp2rHyhgiEiks8SE+J5uE8bHrq8NV+t20mvETNZuW1v2LHyTAVDRCQg13ZuwKuDunL42HEu\nf3oW7yzeEnakPFHBEBEJUMeGlXlneA9a1qnAsFcW8PD7Kwptv4YKhohIwGpUKMXEW7vQv3MDRn++\nnhvHzS2U/RoqGCIiBaBkQhwPXt6aR/q0Zs7677h0xJcs31K4+jVUMERECtA1nRrw2m1dOJp2gj6j\nZvLWosLTr6GCISJSwNo3qMzbw3twVp2K/O/EBTz03grSjp8IO1a2VDBEREJQo3wpXrm1CwO6NGTM\njPXcOO5rvj8Q2/0aKhgiIiEpmRDHA5edxaN92zB3Q6RfY9mWPWHHypIKhohIyK46uz6v396VtONO\n31GzmLZwc9iRMqWCISISA9rVr8Tbw3vQpm4l7nh1IX97Z3nM9WuoYIiIxIjq5ROZcGtnbuzWiLFf\nbuD65+eya/+RsGP9QAVDRCSGlIiP4/5erfjHlW1J/uZ7eo2YydLNsdGvoYIhIhKDruhYj0m3d+WE\nR/o1pi5ICTuSCoaISKxqUy/Sr9GufiV+/doi/vr2co6F2K+hgiEiEsOqlUvk5YGdual7I56fuYEB\nz81hZ0j9GoEWDDOrZGaTzGylma0ws65m9lh0ebGZTTWzSlkce6GZrTKztWZ2d5A5RURiWYn4OP58\naSsev6otC77dTa9/f8mSlILv1wi6hfEkMN3dWwBtgRXAR8BZ7t4GWA38IeNBZhYPPA1cBLQE+plZ\ny4CziojEtD4d6jF5cDfMjL7PzGLyvILt1wisYJhZReAc4DkAdz/q7rvd/UN3T4vuNhuol8nhnYC1\n7r7e3Y8CrwK9g8oqIlJYnFW3Im8N607HBpW5841F3P/WsgLr1wiyhdEYSAXGmdkCMxtrZmUz7HMz\n8H4mx9YFNqVbTomu+xEzG2RmyWaWnJqamh+5RURiWtVyiYy/pRO39GjMC7M20n/sHA4cScv+wDwK\nsmAkAB2AUe7eHjgA/NAXYWb3AmnAhLy8iLuPcfckd0+qXr16Xp5KRKTQSIiP475LWvLE1e1oXLUs\nZUrGB/+aAT53CpDi7nOiy5OIFgwzuxG4BDjf3TP7rsLNQP10y/Wi60REJJ3L2tflsvaZ3oDJd4G1\nMNx9G7DJzJpHV50PLDezC4G7gF7ufjCLw78GmplZYzMrCVwDvBVUVhERyV6QLQyA4cCE6Jv+euAm\nIsUgEfjIzABmu/vtZlYHGOvuF7t7mpkNAz4A4oHn3X1ZwFlFROQUAi0Y7r4QSMqwumkW+24BLk63\n/B7wXnDpRETkdGikt4iI5IgKhoiI5IgKhoiI5IgKhoiI5IgKhoiI5IhlPm6ucDKzVOCbXB5eDdiZ\nj3Hyi3KdHuU6Pcp1eopirobunqNpMopUwcgLM0t294wfAQ6dcp0e5To9ynV6insu3ZISEZEcUcEQ\nEZEcUcH4f2PCDpAF5To9ynV6lOv0FOtc6sMQEZEcUQtDRERyRAVDRERypEgWDDO70MxWmdlaM7s7\nk+2VzWyqmS02s7lmdlZ2x5pZFTP7yMzWRP+tXFC5zKy+mX1qZsvNbJmZ3ZHumPvNbLOZLYz+XJzx\neYPKFd220cyWRF87Od36MM9X83TnY6GZ7TWzX0W35cf5et7MdpjZ0iy2m5k9Fc292Mw6ZPc75dP5\nylWuAri+8nK+gry+cnu+wr6+WpjZV2Z2xMx+m2FbYNcXAO5epH6IfH/GOuAMoCSwCGiZYZ/HgD9H\nH7cAPsnuWOBR4O7o47uBvxdgrtpAh+jj8sDqdLnuB34bxvmKLm8EqmXyvKGdr0yeZxuRwUl5Pl/R\n5ziHyNcPL81i+8VEvqvegC7AnKCvrzzmCuz6ykuuIK+vvOYK+fqqAZwNPJj+tYK+vty9SLYwOgFr\n3X29ux8FXgV6Z9inJfAfAHdfCTQys5rZHNsbeDH6+EXgsoLK5e5b3X1+dP0+YAWQX9/JmJfzdSqh\nna8M+5wPrHP33M4A8CPuPgP47hS79AZe8ojZQCUzq02w11eucwV8feXlfJ1KaOcrwz4Ffn25+w53\n/xo4lmFToNcXFM1bUnWBTemWU/jxxb8I6ANgZp2AhkS+N/xUx9Z0963Rx9uA7N4w8zPXD8ysEdAe\nmJNu9fBok/n5XDQ185rLgY/NbJ6ZDUp3TEycLyJf7zsxw7q8nK+cyCp7kNdXXnL9IIDrK6+5grq+\n8prrpDCur6wEfn0VxYKRE48Q+WthIZGvkV0AHM/pwR5p1wXxeeRT5jKzcsBk4Ffuvje6ehSRJmg7\nYCvwzwLO1cPd2wEXAUPN7JyMB4d4vkoCvYA30h1TEOcrTwI8X6cU4vV1KmFeX6dUHK+voL/TOwyb\ngfrplutF1/0g+j/DTRDp2AI2EPnO8dKnOHb7yeZ7tFm6owBzYWYliPzPPMHdp6Q7ZvvJx2b2LPBO\nQeZy983Rf3eY2VQizeIZhHy+oi4C5qc/R/lwvnIiq+wlslgPeT9feckV5PWVp1wBXl95yhUV1vWV\nlVPlzZfzVRRbGF8DzcyscfQvgGuAt9LvYGaVotsABgIzom8+pzr2LeCG6OMbgGkFlSv6ZvgcsMLd\nH89wTPp7qpcDmX6yIqBcZc2sfHSfskDPdK8f2vlKt0s/MtwuyIfzlRNvAddbRBdgT/R2QJDXV65z\nBXx95SVXkNdXrnOl2x7W9ZWV4K+v3PSUx/oPkU83rCbyiYF7o+tuB26PPu4a3b4KmAJUPtWx0fVV\ngU+ANcDHQJWCygX0INKEXAwsjP5cHN02HlgS3fYWULsAc51BpB9hEbAsVs5XdFtZYBdQMcNz5sf5\nmkjkdsMxIveJb8mQy4Cno7mXAEkFdH3lKlcBXF+5zRX09ZWX/45hXl+1ouv3ArujjysEfX25u6YG\nERGRnCmKt6RERCQAKhgiIpIjKhgiIpIjKhgiIpIjKhgiIpIjRXHgnkjMMLPjRD5mmUBkYOEAd98d\nbiqR3FELQyRYh9y9nbufRWRCuaFhBxLJLRUMkYLzFfk4C6xIQVPBECkAZhZPZCrst7LbVyRWqWCI\nBKt0dDbdk1NKfxRyHpFcU8EQCdYhj0zP3ZDI3ETqw5BCS3NJiQTIzPa7e7no4/bAm0ATd08LN5nI\n6VMLQ6SAuPsCIrOY9gs7i0huqIUhIiI5ohaGiIjkiAqGiIjkiAqGiIjkiAqGiIjkiAqGiIjkiAqG\niIjkiAqGiIjkyP8B9/qoGbTT41YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8be9dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Edata[:,0],Edata[:,4])\n",
    "plt.title(\"Two Electron Energy\")\n",
    "plt.ylabel(\"E/h\")\n",
    "plt.xlabel(\"R\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of individual two-electron terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "L1s,L2s,Lx,Ly,Lz,R1s,R2s,Rx,Ry,Rz = bfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyquante2.ints.two import ERI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Es = []\n",
    "for R in Rs:\n",
    "    n2 = molecule([(7,0,0,-R/2),(7,0,0,R/2)],units='Angstrom')\n",
    "    bfs = basisset(n2,'sto3g')\n",
    "    L1s,L2s,Lx,Ly,Lz,R1s,R2s,Rx,Ry,Rz = bfs\n",
    "    Es.append([ERI(i,i,i,i) for i in bfs])\n",
    "Es = np.array(Es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x9b97668>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VdHAHajMzsxY1cloGAEl7AFcAZ0fE1orFy4HpEbFN0hzgauCAKtuYB8wDmD59estFm5nZ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\n42BKkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x967d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(len(bfs)):\n",
    "    plt.plot(Rs,Es[:,i])\n",
    "plt.title(\"One-center two-electron integral terms\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell shows that the problem isn't the one-center integrals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "Es = []\n",
    "for R in Rs:\n",
    "    n2 = molecule([(7,0,0,-R/2),(7,0,0,R/2)],units='Angstrom')\n",
    "    bfs = basisset(n2,'sto3g')\n",
    "    cross_integrals = []\n",
    "    Es.append(cross_integrals)\n",
    "    for i in bfs[:5]:\n",
    "        for j in bfs[5:]:\n",
    "            cross_integrals.append(ERI(i,i,j,j))\n",
    "    #L1s,L2s,Lx,Ly,Lz,R1s,R2s,Rx,Ry,Rz = bfs\n",
    "Es = np.array(Es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x9fe89b0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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COzx2jq4xt5AS/A9A5vPEO88wdPYnDN+8wWBwgokZ7cMvNWapdZVSt3EDdTsO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\nx/j/AYjMD6led2jnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9e787b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(25):\n",
    "    plt.plot(Rs,Es[:,i])\n",
    "plt.title(\"Two-center coulomb integral terms\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also doesn't appear that the J12 integrals are the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Es = []\n",
    "for R in Rs:\n",
    "    n2 = molecule([(7,0,0,-R/2),(7,0,0,R/2)],units='Angstrom')\n",
    "    bfs = basisset(n2,'sto3g')\n",
    "    cross_integrals = []\n",
    "    Es.append(cross_integrals)\n",
    "    for i in bfs[:5]:\n",
    "        for j in bfs[5:]:\n",
    "            cross_integrals.append(ERI(i,j,i,j))\n",
    "    #L1s,L2s,Lx,Ly,Lz,R1s,R2s,Rx,Ry,Rz = bfs\n",
    "Es = np.array(Es)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xa294ef0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OIsjXqd2z7x+o3c1RvZ4GQPncmAc6agR9kgh2Iwn8QogHuCJeAmd7CJy157Jv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\nROKGkQb+RCKRuGGkgT+RSCRuGP8fc13M+T9Bef0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa1a1ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(25):\n",
    "    plt.plot(Rs,Es[:,i])\n",
    "plt.title(\"Two-center exchange integral terms\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also does not appear that the exchange integrals are the problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I could go on analyzing contributions, but it's unlikely that they make major contributions at this point. Maybe the problem isn't in the integrals themselves, but in the way I'm building the energy out of the integrals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using an explicit SCF cycle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rhf_simple(geo,basisname='sto3g',maxiter=25,verbose=False,guess=None):\n",
    "    bfs = basisset(geo,basisname)\n",
    "    i1 = onee_integrals(bfs,geo)\n",
    "    i2 = twoe_integrals(bfs)\n",
    "    if verbose: print (\"S=\\n%s\" % i1.S)\n",
    "    h = i1.T + i1.V\n",
    "    if verbose: print (\"h=\\n%s\" % h)\n",
    "    if verbose: print (\"T=\\n%s\" % i1.T)\n",
    "    if verbose: print (\"V=\\n%s\" % i1.V)\n",
    "    if guess is None:\n",
    "        E,U = geigh(h,i1.S)\n",
    "    else:\n",
    "        U = guess\n",
    "    if verbose: print (\"E=\\n%s\" % E)\n",
    "    if verbose: print (\"U=\\n%s\" % U)\n",
    "    Enuke = geo.nuclear_repulsion()\n",
    "    nocc = geo.nocc()\n",
    "    Eold = Energy = 0\n",
    "    if verbose: print (\"2e ints\\n%s\" % i2)\n",
    "    for i in xrange(maxiter):\n",
    "        D = dmat(U,nocc)\n",
    "        if verbose: print (\"D=\\n%s\" % D)\n",
    "        Eone = trace2(h,D)\n",
    "        G = i2.get_2jk(D)\n",
    "        H = h+G\n",
    "        Etwo = trace2(H,D)\n",
    "        E,U = geigh(H,i1.S)\n",
    "        Energy = Enuke+Eone+Etwo\n",
    "        if verbose: print (\"HF: %d   %10.4f : %10.4f %10.4f %10.4f\" % ((i+1),Energy,Enuke,Eone,Etwo))\n",
    "        if np.isclose(Energy,Eold):\n",
    "            break\n",
    "        Eold = Energy\n",
    "    else:\n",
    "        print (\"Warning: Maxiter %d hit in scf_simple\" % maxiter)\n",
    "    return Energy,E,U,Eone,Etwo,Enuke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running R=1.000000 ...done; E=-107.419530\n",
      "Running R=1.025000 ...done; E=-107.449971\n",
      "Running R=1.050000 ...done; E=-107.472217\n",
      "Running R=1.100000 ...done; E=-107.496500\n"
     ]
    }
   ],
   "source": [
    "Rs = [1.0,1.025,1.05,1.1]\n",
    "Eterms = []\n",
    "U = None\n",
    "for R in Rs:\n",
    "    print \"Running R=%f ...\" % R,\n",
    "    n2 = molecule([(7,0,0,-R/2),(7,0,0,R/2)],units='Angstrom')\n",
    "    bfs = basisset(n2,'sto3g')\n",
    "    # Run, passing in the previous solution as the input guess\n",
    "    Energy,E,U,Eone,Etwo,Enuke = rhf_simple(n2,guess=U)\n",
    "    Eterms.append((Energy,Eone,Etwo,Enuke))\n",
    "    print \" E=%f\" % Energy\n",
    "Eterms = np.array(Eterms)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xa8e5748>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aI4Z97t4zmDZg77H5GmNuAia5+53B/C1AlrvfY2ZnAUsAA0LA+e5e2NB2dcQg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\nq2iSNXhbpQwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa77d0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Rs,Eterms[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
